Presentation on Making the South African TIMES (SATIM) Energy System Model ‘Water Smart’, by Adrian Stone from the University of Cape Town at 2014 UN-Water Annual International Zaragoza Conference. Preparing for World Water Day 2014: Partnerships for improving water and energy access, efficiency and sustainability. 13-16 January 2014

1. Making the South African TIMES (SATIM)
Energy System Model ‘Water Smart’
Adrian Stone, Bruno Merven & James Cullis (Aurecon)
UN-Water Annual International Zaragoza Conference
Zaragoza, Spain – 13th January 2014

2. Research Groups in ERC
Energy and
climate
change
Energy
efficiency
ERC
Energy
Systems
Analysis
Energy,
poverty and
development

3. South Africa
at a glance…
~52 million people.
GDP ~6000 USD per capita (2011).
~1.2 million m2 (12%) of total land cover suitable for
crop production.
~60% of allocated water consumed for irrigated
agriculture.
~90% of population with access to electricity.
Increasing urbanisation of the population.
Increasing demand for housing and water and
sanitation services.
Eastern
Cape
8%
Highincome,
22%
Gauteng
34%
JHB
Lowincome,
42%
Population distribution by income
Other, 5.5
0%
Industry, 3
%
Municipalrural, 3%
DBN
Midincome,
36%
Mining, 2. Electricity,
2%
50%
KwaZuluNatal
16%
CPT
Western
Cape
14%
Municipalurban, 24
%
Irrigation,
60%
Provincial share of GDP
Estimate of sectorial water use

4. National Water Resource Strategy
4

5. Water Marginal Cost Curves
Reconciliation of future demand and potential augmentation options for the Lephalale WMA
5

6. Water Supply Cost Curves
Provisional costs for future water supply augmentation in the Lephalale catchment
6

7. Water Supply Cost Curves under
Climate Change Risk
Potential impact of climate change on cost of future water supply options in the Lephalale WMA.
7

8. National Energy Model – Key Points
• South African TIMES Model (SATIM)
• Partial equilibrium linear least-cost optimisation model
capable of representing the whole energy
system, including its economic costs and its emissions.
• A number of years of development - 2003 IEP, 2007 LTMS
• Sectoral Representation - Electricity & Transport sector
represented in most detail.
• Methodology & Assumptions in the public domain •
•
•
http://www.erc.uct.ac.za/Research/Otherdocs/Satim/SATIM%20Methodologyv2.1.pdf
http://www.erc.uct.ac.za/Research/esystems-group-satim.htm
http://www.erc.uct.ac.za/Research/publications/12-Mervenetal_Quantifying_energy_needs_transport%20sector.pdf
8

9. Main Features
• Bottom-up Energy Systems Least-Cost Optimization Long-Range
(>10 years) Planning Model (similar to the one used for the IEP)
• Full Sector: Includes and allows trade-off between demand and
Supply
• End-use type model:
– Gives a detailed description of how the energy is used.
– Describes the types of equipment used and how much energy is used by
each type of equipment to satisfy demand.
– Can be used to forecast useful energy as well as final energy demand
– Can capture:
•
•
•
•
•
structural changes/ shocks
mode switching (transport)
fuel switching
Technical improvement/ improved efficiency
Intensity changes e.g. mines have to dig deeper
Objective – Minimise the cost of supplying an energy service

10. TIMES – represent & cost entire energy system – cost
optimal pathways under constraints
10

11. Energy model components
• Made up of 2 simple components:
– Energy Carriers (e.g. fuels, demand)
– Technologies (e.g. Light bulb, power plant) all
characterized in the same way:
– Input and Output Carrier (Commodity)
These are the
– Efficiency
parameters
– Investment Costs per unit of capacity
that affect the
– Activity Costs
– Existing Capacity
cost of
– Annual Availability
supplying the
– Expected Life
– Emissions
energy service

12. Simple Reference Energy System
Commodities are input to and output from technologies along competing chains to
supply an energy service. Water can be one of these commodities if we know enough
about supply.

13. Scenarios we will look at…..
• Optimisation results with and without water
costs.
• Climate change impacts on water supply cost
curves
• 4 GHG constrained scenarios – contrasting 275
Mton cap on the power sector with CO2 tax
options based on National Treasury’s
proposed tax structure.

14. Shortcomings of TIMES
• No demand response (unless used with elastic
demand, in which case a price elasticity is
needed for each end-use)
• Impacts on the rest of the economy, and
socio-economic indicators not quantified
• For answering broad techno-economic
planning questions – neither a lot of
engineering or economic detail. Can be reliant
on good micro-analysis.